{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:07:27Z","timestamp":1774966047518,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100007297","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-18-1-2875"],"award-info":[{"award-number":["N00014-18-1-2875"]}],"id":[{"id":"10.13039\/100007297","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,12]]},"DOI":"10.1145\/3394171.3413827","type":"proceedings-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T12:26:18Z","timestamp":1602505578000},"page":"2682-2690","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":172,"title":["Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning"],"prefix":"10.1145","author":[{"given":"Wentao","family":"Bao","sequence":"first","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY, USA"}]},{"given":"Qi","family":"Yu","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY, USA"}]},{"given":"Yu","family":"Kong","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Weight Uncertainty in Neural Networks. In International Conference on Machine Learning.","author":"Blundell Charles","year":"2015","unstructured":"Charles Blundell , Julien Cornebise , Koray Kavukcuoglu , and Daan Wierstra . 2015 . Weight Uncertainty in Neural Networks. In International Conference on Machine Learning. Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight Uncertainty in Neural Networks. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_2_1","volume-title":"Quasi-Recurrent Neural Networks. In International Conference on Learning Representations.","author":"Bradbury James","year":"2017","unstructured":"James Bradbury , Stephen Merity , Caiming Xiong , and Richard Socher . 2017 . Quasi-Recurrent Neural Networks. In International Conference on Learning Representations. James Bradbury, Stephen Merity, Caiming Xiong, and Richard Socher. 2017. Quasi-Recurrent Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_3_1","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.","author":"Cai Zhaowei","year":"2018","unstructured":"Zhaowei Cai and Nuno Vasconcelos . 2018 . Cascade R-CNN: Delving into High Quality Object Detection . In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Zhaowei Cai and Nuno Vasconcelos. 2018. Cascade R-CNN: Delving into High Quality Object Detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_4_1","volume-title":"Anticipating Accidents in Dashcam Videos. In Asian Conference on Computer Vision.","author":"Chan Fu-Hsiang","year":"2016","unstructured":"Fu-Hsiang Chan , Yu-Ting Chen , Yu Xiang , and Min Sun . 2016 . Anticipating Accidents in Dashcam Videos. In Asian Conference on Computer Vision. Fu-Hsiang Chan, Yu-Ting Chen, Yu Xiang, and Min Sun. 2016. Anticipating Accidents in Dashcam Videos. In Asian Conference on Computer Vision."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_2_2_6_1","volume-title":"Proceedings of Neural Information Processing Systems.","author":"Chung Junyoung","year":"2015","unstructured":"Junyoung Chung , Kyle Kastner , Laurent Dinh , Kratarth Goel , Aaron C Courville , and Yoshua Bengio . 2015 . A Recurrent Latent Variable Model for Sequential Data . In Proceedings of Neural Information Processing Systems. Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio. 2015. A Recurrent Latent Variable Model for Sequential Data. In Proceedings of Neural Information Processing Systems."},{"key":"e_1_3_2_2_7_1","volume-title":"Traffic Risk Assessment: A Two-Stream Approach Using Dynamic Attention. In Conference on Computer and Robot Vision.","author":"Corcoran G.","unstructured":"G. Corcoran and J. Clark . 2019 . Traffic Risk Assessment: A Two-Stream Approach Using Dynamic Attention. In Conference on Computer and Robot Vision. G. Corcoran and J. Clark. 2019. Traffic Risk Assessment: A Two-Stream Approach Using Dynamic Attention. In Conference on Computer and Robot Vision."},{"key":"e_1_3_2_2_8_1","volume-title":"Proceedings of Neural Information Processing Systems.","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard , Xavier Bresson , and Pierre Vandergheynst . 2016 . Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering . In Proceedings of Neural Information Processing Systems. Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Proceedings of Neural Information Processing Systems."},{"key":"e_1_3_2_2_9_1","volume-title":"Denker and Yann LeCun","author":"John","year":"1990","unstructured":"John S. Denker and Yann LeCun . 1990 . Transforming Neural-Net Output Levels to Probability Distributions. In Proceedings of Neural Information Processing Systems . John S. Denker and Yann LeCun. 1990. Transforming Neural-Net Output Levels to Probability Distributions. In Proceedings of Neural Information Processing Systems."},{"key":"e_1_3_2_2_10_1","volume-title":"IEEE Intelligent Transportation Systems Conference.","author":"Fang J.","unstructured":"J. Fang , D. Yan , J. Qiao , J. Xue , H. Wang , and S. Li . 2019. DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark . In IEEE Intelligent Transportation Systems Conference. J. Fang, D. Yan, J. Qiao, J. Xue, H. Wang, and S. Li. 2019. DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark. In IEEE Intelligent Transportation Systems Conference."},{"key":"e_1_3_2_2_11_1","volume-title":"Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. In International Conference on Learning Representations (Workshop).","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani . 2016 . Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. In International Conference on Learning Representations (Workshop). Yarin Gal and Zoubin Ghahramani. 2016. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. In International Conference on Learning Representations (Workshop)."},{"key":"e_1_3_2_2_12_1","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.","author":"Geiger Andreas","year":"2012","unstructured":"Andreas Geiger , Philip Lenz , and Raquel Urtasun . 2012 . Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite . In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012. Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_13_1","volume-title":"Proceedings of Neural Information Processing Systems.","author":"Graves Alex","year":"2011","unstructured":"Alex Graves . 2011 . Practical Variational Inference for Neural Networks . In Proceedings of Neural Information Processing Systems. Alex Graves. 2011. Practical Variational Inference for Neural Networks. In Proceedings of Neural Information Processing Systems."},{"key":"e_1_3_2_2_14_1","volume-title":"Proceedings of Neural Information Processing Systems.","author":"Hajiramezanali Ehsan","year":"2019","unstructured":"Ehsan Hajiramezanali , Arman Hasanzadeh , Krishna Narayanan , Nick Duffield , Mingyuan Zhou , and Xiaoning Qian . 2019 . Variational Graph Recurrent Neural Networks . In Proceedings of Neural Information Processing Systems. Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, and Xiaoning Qian. 2019. Variational Graph Recurrent Neural Networks. In Proceedings of Neural Information Processing Systems."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_16_1","volume-title":"Proceedings of Neural Information Processing Systems.","author":"Kendall Alex","year":"2017","unstructured":"Alex Kendall and Yarin Gal . 2017 . What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? . In Proceedings of Neural Information Processing Systems. Alex Kendall and Yarin Gal. 2017. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?. In Proceedings of Neural Information Processing Systems."},{"key":"e_1_3_2_2_17_1","volume-title":"Auto-Encoding Variational Bayes. In International Conference on Learning Representations.","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013 . Auto-Encoding Variational Bayes. In International Conference on Learning Representations. Diederik P Kingma and Max Welling. 2013. Auto-Encoding Variational Bayes. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_18_1","volume-title":"Proceedings of Neural Information Processing Systems (Workshop).","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016 . Variational Graph Auto-Encoders . In Proceedings of Neural Information Processing Systems (Workshop). Thomas N Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. In Proceedings of Neural Information Processing Systems (Workshop)."},{"key":"e_1_3_2_2_19_1","volume-title":"Semi-supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling . 2017 . Semi-supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. Thomas N Kipf and Max Welling. 2017. Semi-supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_20_1","volume-title":"Beom Joon Kim, and Myunghee Cho Paik","author":"Kwon Yongchan","year":"2018","unstructured":"Yongchan Kwon , Joong-Ho Won , Beom Joon Kim, and Myunghee Cho Paik . 2018 . Uncertainty Quantification Using Bayesian Neural Networks in Classification : Application to Ischemic Stroke Lesion Segmentation. In Medical Imaging with Deep Learning . Yongchan Kwon, Joong-Ho Won, Beom Joon Kim, and Myunghee Cho Paik. 2018. Uncertainty Quantification Using Bayesian Neural Networks in Classification: Application to Ischemic Stroke Lesion Segmentation. In Medical Imaging with Deep Learning."},{"key":"e_1_3_2_2_21_1","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.","author":"Lin Tsung-Yi","year":"2017","unstructured":"Tsung-Yi Lin , Piotr Doll\u00e1r , Ross Girshick , Kaiming He , Bharath Hariharan , and Serge Belongie . 2017 . Feature Pyramid Networks for Object Detection . In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Tsung-Yi Lin, Piotr Doll\u00e1r, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. 2017. Feature Pyramid Networks for Object Detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_22_1","volume-title":"A Critical Review of Recurrent Neural Networks for Sequence Learning. arXiv:1506.00019","author":"Lipton Zachary C","year":"2015","unstructured":"Zachary C Lipton , John Berkowitz , and Charles Elkan . 2015. A Critical Review of Recurrent Neural Networks for Sequence Learning. arXiv:1506.00019 ( 2015 ). Zachary C Lipton, John Berkowitz, and Charles Elkan. 2015. A Critical Review of Recurrent Neural Networks for Sequence Learning. arXiv:1506.00019 (2015)."},{"key":"e_1_3_2_2_23_1","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.","author":"Ma Shugao","year":"2016","unstructured":"Shugao Ma , Leonid Sigal , and Stan Sclaroff . 2016 . Learning Activity Progression in LSTMs for Activity Detection and Early Detection . In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Shugao Ma, Leonid Sigal, and Stan Sclaroff. 2016. Learning Activity Progression in LSTMs for Activity Detection and Early Detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_24_1","volume-title":"Bayesian learning for neural networks","author":"Neal Radford M","unstructured":"Radford M Neal . 2012. Bayesian learning for neural networks . Vol. 118 . Springer Science & Business Media . Radford M Neal. 2012. Bayesian learning for neural networks. Vol. 118. Springer Science & Business Media."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00354"},{"key":"e_1_3_2_2_26_1","volume-title":"Proceedings of Neural Information Processing Systems.","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , Alban Desmaison , Andreas Kopf , Edward Yang , Zachary DeVito , Martin Raison , Alykhan Tejani , Sasank Chilamkurthy , Benoit Steiner , Lu Fang , Junjie Bai , and Soumith Chintala . 2019 . PyTorch: An Imperative Style, High-Performance Deep Learning Library . In Proceedings of Neural Information Processing Systems. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Proceedings of Neural Information Processing Systems."},{"key":"e_1_3_2_2_27_1","volume-title":"Proceedings of Neural Information Processing Systems.","author":"Ren Shaoqing","year":"2015","unstructured":"Shaoqing Ren , Kaiming He , Ross Girshick , and Jian Sun . 2015 . Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks . In Proceedings of Neural Information Processing Systems. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In Proceedings of Neural Information Processing Systems."},{"key":"e_1_3_2_2_28_1","volume-title":"Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In International Conference on Machine Learning.","author":"Rezende Danilo Jimenez","year":"2014","unstructured":"Danilo Jimenez Rezende , Shakir Mohamed , and Daan Wierstra . 2014 . Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In International Conference on Machine Learning. Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2018.8639160"},{"key":"e_1_3_2_2_31_1","volume-title":"Uncertainty Estimations by Softplus Normalization in Bayesian Convolutional Neural Networks with Variational Inference. arXiv:1806.05978","author":"Shridhar Kumar","year":"2018","unstructured":"Kumar Shridhar , Felix Laumann , and Marcus Liwicki . 2018. Uncertainty Estimations by Softplus Normalization in Bayesian Convolutional Neural Networks with Variational Inference. arXiv:1806.05978 ( 2018 ). Kumar Shridhar, Felix Laumann, and Marcus Liwicki. 2018. Uncertainty Estimations by Softplus Normalization in Bayesian Convolutional Neural Networks with Variational Inference. arXiv:1806.05978 (2018)."},{"key":"e_1_3_2_2_32_1","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.","author":"Suzuki Tomoyuki","year":"2018","unstructured":"Tomoyuki Suzuki , Hirokatsu Kataoka , Yoshimitsu Aoki , and Yutaka Satoh . 2018 . Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB . In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Tomoyuki Suzuki, Hirokatsu Kataoka, Yoshimitsu Aoki, and Yutaka Satoh. 2018. Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3356995.3364535"},{"key":"e_1_3_2_2_34_1","volume-title":"Proceedings of Neural Information Processing Systems.","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N. Gomez , Lukasz Kaiser , and Illia Polosukhin . 2017 . Attention is All You Need . In Proceedings of Neural Information Processing Systems. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Proceedings of Neural Information Processing Systems."},{"key":"e_1_3_2_2_35_1","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.","author":"Xie Saining","year":"2017","unstructured":"Saining Xie , Ross Girshick , Piotr Doll\u00e1r , Zhuowen Tu , and Kaiming He . 2017 . Aggregated Residual Transformations for Deep Neural Networks . In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Saining Xie, Ross Girshick, Piotr Doll\u00e1r, Zhuowen Tu, and Kaiming He. 2017. Aggregated Residual Transformations for Deep Neural Networks. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_36_1","volume-title":"Unsupervised Traffic Accident Detection in First-person Videos. In International Conference on Intelligent Robots and Systems.","author":"Yao Yu","year":"2019","unstructured":"Yu Yao , Mingze Xu , Yuchen Wang , David J Crandall , and Ella M Atkins . 2019 . Unsupervised Traffic Accident Detection in First-person Videos. In International Conference on Intelligent Robots and Systems. Yu Yao, Mingze Xu, Yuchen Wang, David J Crandall, and Ella M Atkins. 2019. Unsupervised Traffic Accident Detection in First-person Videos. In International Conference on Intelligent Robots and Systems."},{"key":"e_1_3_2_2_37_1","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.","author":"Yu Fisher","year":"2020","unstructured":"Fisher Yu , Haofeng Chen , Xin Wang , Wenqi Xian , Yingying Chen , Fangchen Liu , Vashisht Madhavan , and Trevor Darrell . 2020 . BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning . In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell. 2020. BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_38_1","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.","author":"Zeng Kuo-Hao","year":"2017","unstructured":"Kuo-Hao Zeng , Shih-Han Chou , Fu-Hsiang Chan , Juan Carlos Niebles , and Min Sun . 2017 . Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization . In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Kuo-Hao Zeng, Shih-Han Chou, Fu-Hsiang Chan, Juan Carlos Niebles, and Min Sun. 2017. Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00698"}],"event":{"name":"MM '20: The 28th ACM International Conference on Multimedia","location":"Seattle WA USA","acronym":"MM '20","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 28th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394171.3413827","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394171.3413827","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394171.3413827","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:17Z","timestamp":1750197677000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394171.3413827"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":39,"alternative-id":["10.1145\/3394171.3413827","10.1145\/3394171"],"URL":"https:\/\/doi.org\/10.1145\/3394171.3413827","relation":{},"subject":[],"published":{"date-parts":[[2020,10,12]]},"assertion":[{"value":"2020-10-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}